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Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment's state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time–frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time–frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura–Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark. [ABSTRACT FROM AUTHOR]

Power conversion systems for wireless power transfer (WPT) applications have demanding requirements for continuity of service, besides being operated with stressing environmental conditions. Diagnostic and prognostic programs are thus quite useful and this work shows a novel approach based on the analysis of spectra of an autoregressive (AR) model to recognize a wide range of faulty devices, including incipient faults, when deviations from nominal parameters begin to manifest. AR modeling provides cleaner and easier to interpret spectra, where only the salient features remain, and they are more sensitive to variations in the corresponding time domain waveforms. A log spectral distance is calculated that successfully separates healthy and faulty states of the feeding single-phase inverter, even in challenging scenarios of poor signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a new recursive identification algorithm for autoregressive (AR) models corrupted by additive white noise. The proposed approach relies on a set of both low-order and high-order Yule-Walker equations and on a modified version of the overdetermined recursive instrumental variable method, leading to the estimation of both the AR coefficients and the additive noise variance. The main motivation behind our proposition is introducing model identification procedures suitable for implementation on edge-computing platforms and programmable logic controllers (PLCs), which are known to have limited capabilities and resources when dealing with complex mathematical computations (i.e., matrix inversion). Indeed, our development is focused on condition monitoring systems, with particular attention paid to their integration onboard industrial machinery. The performance of the recursive approach is tested using both numerical simulations and a laboratory case study. The obtained results are very promising. [ABSTRACT FROM AUTHOR]

This work aims to provide useful insights into the course of action and the challenges faced by machine manufacturers when dealing with the actual application of Prognostics and Health Management procedures in industrial environments. Taking into account the computing capabilities and connectivity of the hardware available for smart manufacturing, we propose a particular solution that allows meeting one of the essential requirements of intelligent production processes, i.e., autonomous health management. Indeed, efficient and fast algorithms, that does not require a high computational cost and can be appropriately performed on machine controllers, i.e., on edge, are combined with others, which can handle large amounts of data and calculations, executed on remote powerful supervisory platforms, i.e., on the cloud. In detail, new condition monitoring algorithms based on Model-of-Signals techniques are developed and implemented on local controllers to process the raw sensor readings and extract meaningful and compact features, according to System Identification rules and guidelines. These results are then transmitted to remote supervisors, where Particle Filters are exploited to model components degradation and predict their Remaining Useful Life. Practitioners can use this information to optimise production planning and maintenance policies. The proposed architecture allows keeping the communication traffic between edge and cloud in the nowadays affordable "Big data" range, preventing the unmanageable "Huge data" scenario that would follow from the transmission of raw sensor data. Furthermore, the robustness and effectiveness of the proposed method are tested considering a meaningful benchmark, the PRONOSTIA dataset, allowing reproducibility and comparison with other approaches. [ABSTRACT FROM AUTHOR]

Power and thermal management are critical components of high-performance-computing (HPC) systems, due to their high-power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly from the thermal response of the final device enables more robust and precise thermal control strategies as well as automated diagnosis. However, when dealing with large-scale systems “in production,” the accuracy of learned thermal models depends on the dynamics of the power excitation, which depends also on the executed workload, and measurement nonidealities such as quantization. In this article we show that, using an advanced system identification algorithm, we are able to generate very accurate thermal models (average error lower than our sensors quantization step of 1 °C) for a large-scale HPC system on real workloads for very long time periods. However, we also show that: 1) not all real workloads allow for the identification of a good model and 2) starting from the theory of system identification it is very difficult to evaluate if a trace of data leads to a good estimated model. We then propose and validate a set of techniques based on machine learning and deep learning algorithms for the choice of data traces to be used for model identification. We also show that deep learning techniques are absolutely necessary to correctly choose such traces up to 96% of the times. [ABSTRACT FROM AUTHOR]

Thermal-aware design and online optimization of the cooling effort are becoming increasingly important in current and future high-performance computing (HPC) systems. A fundamental requirement to effectively develop such techniques is the availability of distributed and compact models representing the system thermal behavior. System identification algorithms allow to extract models directly from the thermal response of the target device. This article proposes a novel thermal identification approach for real, in-production HPC systems, which is capable of extracting thermal models from a computing node affected by quantization noise on the temperature measurements as well as operating in the free-cooling mode, with variable ambient temperature. The approach allows also to identify the physical floorplan of the CPU dies in supercomputing nodes. The effectiveness of the proposed methodology has been tested on a node of the CINECA Galileo Tier-1 supercomputer system. [ABSTRACT FROM AUTHOR]

This paper deals with the identification of an autoregressive (AR) process disturbed by an additive moving-average (MA) noise. Our approach operates as follows: Firstly, the AR parameters are estimated by using the overdetermined high-order Yule-Walker equations. The variance of the AR process driving process can be deduced by means of an orthogonal projection between two types of estimates of AR process correlation vectors. Then, the correlation sequence of the MA noise is estimated. Secondly, the MA parameters are obtained by using inner-outer factorization. To study the relevance of the resulting method, we compare it with existing algorithms, and we analyze the identifiability limits. The identification approach is then combined with Kalman filtering for channel estimation in mobile communication systems. [ABSTRACT FROM AUTHOR]

SUMMARY This paper proposes a bias-eliminating least-squares (BELS) approach for identifying linear dynamic errors-in-variables (EIV) models whose input and output are corrupted by additive white noise. The method is based on an iterative procedure involving, at each step, the estimation of both the system parameters and the noise variances. The proposed identification algorithm differs from previous BELS algorithms in two aspects. First, the input and output noises are allowed to be mutually correlated, and second, the estimation of the noise covariances is obtained by exploiting the statistical properties of the equation error of the EIV model. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Autoregressive (AR) models play a role of paramount importance in the description of scalar and multivariate time series and find many applications in prediction and filtering. The main limit of AR models is associated with their elementary description of the misfit between observations and model (equation error considered as a white noise). A more realistic family of autoregressive models is given by 'AR+noise' ones where besides a white equation error also additive white noise on the observations is considered. Noisy AR models have given very good results in practical applications and lead to more realistic descriptions of the underlying processes; for these reasons, they are intrinsically more suitable than AR models for filtering applications. The use of AR+noise models in filtering requires the construction of a state-space realization and Kalman filtering. This article proposes an efficient innovation-based filtering approach whose computational burden is lower than that of Kalman filtering. The proposed algorithm relies directly on polynomial input-output models and on Cholesky factorization. [ABSTRACT FROM AUTHOR]

This correspondence describes a method for identifying FIR models in the presence of input and output noise. The proposed algorithm takes advantage of both the bias compensation principle and the instrumental variable method. It is based on a nonlinear system of equations whose unkowns are the FIR coefficients and the input noise variance. This system allows mapping the noisy FIR identification problem into a quadratic eigenvalue problem. The identification problem is thus solved without requiring the use of iterative least-squares algorithms. The performance of the proposed approach has been tested and compared with that of other identification methods by means of Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

A new bias-compensated least-squares method for identifying finite impulse response (FIR) models whose input and output are affected by additive white noise is proposed. By exploiting the statistical properties of the equation error of the noisy FIR system, an estimate of the input noise variance is obtained and the noise-induced bias is removed. The results obtained by means of Monte Carlo simulations show that the proposed algorithm outperforms other bias-compensated approaches and allows to obtain an estimation accuracy comparable to that of total least-squares without requiring the a priori knowledge of the input-output noise variance ratio. [ABSTRACT FROM AUTHOR]

In the framework of speech enhancement, several parametric approaches based on an a priori model for a speech signal have been proposed. When using an autoregressive (AR) model, three issues must be addressed. 1) How to deal with AR parameter estimation? Indeed, due to additive noise, the standard least squares criterion leads to biased estimates of AR parameters. 2) Can an estimation of the variance of the additive noise for each speech frame be obtained? A voice activity detector is often used for its estimation. 3) Which estimation rules and techniques (filtering, smoothing, etc.) can be considered to retrieve the speech signal? Our contribution in this paper is threefold. First, we propose to view the identification of the noisy AR process as an errors-in-variables problem. This blind method has the advantage of providing accurate estimations of both the AR parameters and the variance of the additive noise. Second, we propose an alternative algorithm to standard Kalman smoothing, based on a constrained minimum variance estimation procedure with a lower computational cost. Third, the combination of these two steps is investigated. It provides better results than some existing speech enhancement approaches in terms of signal-to-noise-ratio (SNR), segmental SNR, and informal subjective tests. [ABSTRACT FROM AUTHOR]